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Measures of the Geographic Concentration of Industries: Improving Distance-Based Methods

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  • Eric Marcon

    (ECOFOG - Ecologie des forêts de Guyane - CIRAD : UMR93 - CNRS : UMR2728 - INRA : UMR0745 - Université des Antilles et de Guyane - AgroParisTech)

  • Florence Puech

    (LET - Laboratoire d'économie des transports - CNRS : UMR5593 - Université Lumière - Lyon II - Ecole Nationale des Travaux Publics de l'Etat)

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    Abstract

    This study introduces two new measures of spatial concentration. The proposed M functions constitute an extension to Ripley's functions (Ripley, 1976, 1977). They allow the evaluation of the relative geographic concentration and co-location of industries in a non-homogeneous spatial framework. Some rigorous comparisons with similar recently developed tools prove the relevance of the M functions in the field of spatial economics.

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    Bibliographic Info

    Paper provided by HAL in its series Working Papers with number halshs-00372617.

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    Date of creation: 01 Apr 2009
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    Handle: RePEc:hal:wpaper:halshs-00372617

    Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00372617
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    Related research

    Keywords: Geographic concentration ; Distance-based methods ; Ripley's K function ; M function;

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    References

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    1. Eric Marcon & Florence Puech, 2003. "Evaluating the geographic concentration of industries using distance-based methods," Journal of Economic Geography, Oxford University Press, vol. 3(4), pages 409-428, October.
    2. Duranton, Gilles & Henry G Overman, 2003. "Testing for Localisation Using Micro-Geographic Data," Royal Economic Society Annual Conference 2003 69, Royal Economic Society.
    3. Haaland, J.I. & Kind, H.J. & knarvik, K.H.M. & Torstensson, J., 1998. "What Determines the Economic Geography of Europe?," Papers 19/98, Norwegian School of Economics and Business Administration-.
    4. Gilles Duranton & Henry G. Overman, 2008. "Exploring The Detailed Location Patterns Of U.K. Manufacturing Industries Using Microgeographic Data," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 213-243.
    5. Tomoya Mori & Koji Nishikimi & Tony E. Smith, 2004. "A Divergence Statistic for Industrial Localization," KIER Working Papers 587, Kyoto University, Institute of Economic Research.
    6. Pierre-Philippe Combes & Henry Overman, 2003. "The Spatial Distribution of Economic Activities in the European Union," CEP Discussion Papers dp0587, Centre for Economic Performance, LSE.
    7. Briant, A. & Combes, P.-P. & Lafourcade, M., 2010. "Dots to boxes: Do the size and shape of spatial units jeopardize economic geography estimations?," Journal of Urban Economics, Elsevier, vol. 67(3), pages 287-302, May.
    8. Giuseppe Arbia & Giuseppe Espa & Danny Quah, 2008. "A class of spatial econometric methods in the empirical analysis of clusters of firms in the space," Empirical Economics, Springer, vol. 34(1), pages 81-103, February.
    9. Ugo Fratesi, 2008. "Issues in the measurement of localization," Environment and Planning A, Pion Ltd, London, vol. 40(3), pages 733-758, March.
    10. Rosenthal, Stuart S. & Strange, William C., 2004. "Evidence on the nature and sources of agglomeration economies," Handbook of Regional and Urban Economics, in: J. V. Henderson & J. F. Thisse (ed.), Handbook of Regional and Urban Economics, edition 1, volume 4, chapter 49, pages 2119-2171 Elsevier.
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    Cited by:
    1. Arbia, G. & Espa, G. & Giuliani, D. & Mazzitelli, A., 2012. "Clusters of firms in an inhomogeneous space: The high-tech industries in Milan," Economic Modelling, Elsevier, vol. 29(1), pages 3-11.
    2. Pablo Jensen & Julien Michel, 2011. "Measuring spatial dispersion: exact results on the variance of random spatial distributions," The Annals of Regional Science, Springer, vol. 47(1), pages 81-110, August.

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